Welcome to Yan Xia's Page ！

Welcome to my website!

• Education

With an intense interest in atmospheric modeling and climate change research, after receiving my bachelor’s degree at Beijing Jiaotong University, I joined Professor Shaocai Yu’s air pollution and climate change research group at Zhejiang University.

• Research Experience

I have experience in determining the effects of popularizing electric vehicles on air quality using chemical transport models (WRF-CMAQ) and assessing on-road vehicle-specific emissions, as well as predicting spatiotemporal evolution of hyperfine-resolution mapping of urban road emissions based on machine learning (ML) algorithms. For more details, please go to my research page.

• Research Interests

I would like to tackle climate change with ML in my future studies.

1. Hybrid model. Build a hybrid model by combining physical-processed models and data-driven ML to represent subgrid processes (e.g., cloud microphysics and convection); predict extreme events (e.g., extreme precipitation); and improve understanding of aerosol-cloud-climate interactions.

2. Data-constrained hybrid model. Tackle earth monitoring data (e.g., remote sensing) with ML by retrieving and inverting vital biophysical parameters to correct the established hybrid model.

3. Uncertainty estimation and model interpretability. Develop theories and methods (e.g., Bayesian/probabilistic inference) to better understand the established hybrid model and fundamental scientific questions.

4. Physics-informed ML(NN). Embed Physics-informed ML(NN) in all above processes by adding physical constraints in the loss function or modifying the architecture of neural networks to improve model accuracy and deepen the development of ML.